62 research outputs found
Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization
Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavors’ ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimizing flavors to maximize liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors
Genetic Representations for Evolutionary Minimization of Network Coding Resources
We demonstrate how a genetic algorithm solves the problem of minimizing the
resources used for network coding, subject to a throughput constraint, in a
multicast scenario. A genetic algorithm avoids the computational complexity
that makes the problem NP-hard and, for our experiments, greatly improves on
sub-optimal solutions of established methods. We compare two different genotype
encodings, which tradeoff search space size with fitness landscape, as well as
the associated genetic operators. Our finding favors a smaller encoding despite
its fewer intermediate solutions and demonstrates the impact of the modularity
enforced by genetic operators on the performance of the algorithm.Comment: 10 pages, 3 figures, accepted to the 4th European Workshop on the
Application of Nature-Inspired Techniques to Telecommunication Networks and
Other Connected Systems (EvoCOMNET 2007
The Facebook Algorithm's Active Role in Climate Advertisement Delivery
Communication strongly influences attitudes on climate change. Within
sponsored communication, high spend and high reach advertising dominates. In
the advertising ecosystem we can distinguish actors with adversarial stances:
organizations with contrarian or advocacy communication goals, who direct the
advertisement delivery algorithm to launch ads in different destinations by
specifying targets and campaign objectives. We present an observational
(N=275,632) and a controlled (N=650) study which collectively indicate that the
advertising delivery algorithm could itself be an actor, asserting
statistically significant influence over advertisement destinations,
characterized by U.S. state, gender type, or age range. This algorithmic
behaviour may not entirely be understood by the advertising platform (and its
creators). These findings have implications for climate communications and
misinformation research, revealing that targeting intentions are not always
fulfilled as requested and that delivery itself could be manipulated
- …